AI role · Open offer
LLM Engineer — legal document AI
Luminance
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At a glance
How you'll work, where, and key listing dates.
- Employment
- Full-time
- Workplace
- Hybrid
- Location
- 30 Old Bailey, London, England EC4M 7AU, United Kingdom · Hybrid
- Compensation
- £95k–£130k + equity
- Experience level
- Senior
- Team
- Legal AI
- Benefits & perks
About this role
What the team is looking for and how this position fits.
Luminance is hiring a Senior LLM Engineer — legal document AI on the Legal AI team (Hybrid · London, United Kingdom). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.
You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.
Luminance applies AI to contract review, due diligence, and compliance for global law firms and in-house legal teams.
LLM Engineer (London): RAG over privileged documents, citation fidelity, and workflow integrations with iManage / NetDocuments.
You will balance model quality with auditability—every answer must be traceable to source clauses.
What you will do
- Build retrieval pipelines with chunking strategies tuned for legal PDFs and tables.
- Implement guardrails for privilege, jurisdiction, and hallucination thresholds.
- Run offline evals with attorney-reviewed golden sets before beta releases.
- Collaborate with customer success on EU/UK data residency requirements.
Stack & environment
- Python, LangChain, vector databases, OpenAI / Anthropic APIs.
- Dockerised services, hybrid London (Old Bailey area).
What we look for
- Production RAG with citation or grounding requirements.
- Comfort discussing precision/recall trade-offs with non-ML stakeholders.
- Bonus: NLP on long documents or multilingual corpora.
Skills we're hiring for
What each capability means for this role—not a keyword checklist.
- Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
- LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
- OpenAI API
Tools & stack
Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).
- DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.
You're a strong fit if…
Tailored to this listing—use it as a self-check before you apply.
- ✓You can show evidence in your application: RAG or contract-review workflow with citation requirements.
- ✓Several of these show up in recent shipped work—not only on your CV: Python, LangChain, OpenAI API, Vector DBs, and Docker.
- ✓You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
- ✓You are comfortable with the hybrid rhythm (Hybrid · London, United Kingdom)—onsite collaboration when it matters, deep work when remote.
- ✓You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
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What the employer asked for
RAG or contract-review workflow with citation requirements.
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